Lesson y learning Study y la idea del docente como investigador

Resumen tomado de la publicaci??n

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

DTM generation from LIDAR data using skewness balancing

LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications.

Too much information? : Drama teachers’ perceptions of the value of subject knowledge

This paper reports on research into what drama teachers consider they really need to know as drama specialists. In the first instance the very concept of knowledge is discussed as it pertains to education in the arts as is the current situation in England regarding the extent to which new drama teachers’ subject specialist knowledge has been formally accredited and what the implications of this may be to an evolving curriculum. The research itself initially involved using a questionnaire to investigate the way in which drama teachers prioritised different aspects of professional knowledge. Results of this survey were deemed surprising enough to warrant further investigation through the use of interviews and a multiple-sorting exercise which revealed why the participants prioritised in the way they did. Informed by the work of Bourdieu, Foucault and Kelly, a model is proposed which may help explain the tensions experienced by drama teachers as they try to balance and prioritise different aspects of professional knowledge.

A sensor based intelligent building assessment model

While building provides shelter for human being, the previous models for assessing the intelligence of a building seldom consider the responses of occupants. In addition, the assessment is usually conducted by an authority organization on a yearly basis, thus can seldom provide timely assistance for facility manager to improve his daily facility maintenance performance. By the extending the law of entropy into the area of intelligent building, this paper demonstrate that both energy consumption and the response of occupants are important when partially assessing the intelligence of a building. This study then develops a sensor based real time building intelligence (BI) assessment model. An experimental case study demonstrates how the model can be implemented. The developed model can address the two demerits of the previous BI assessment model.